My primary observation is people who engage these systems as finished products experience less value than people who engage them as tools that can be used to make other things. I am increasingly curious about how we foster engagement that causes people to treat tools as capable of creating more tools, and sharing what they make and learn. Let’s get weird...and start off with some math.

Compounding Curves

Compound interest is a simple idea: the return in each period applies to the accumulated total, not just the original principal. The result is that growth is multiplicative, not additive. You’ve seen the curve. It looks unremarkable until it does not. I believe this pattern applies to people who employ tools with atypical expectations.

When someone uses a tool, they extract value. When someone uses a tool to build a better tool, they change the base to which all future returns apply. When that better tool is then used to build yet another tool, the compounding accelerates. The person in the second regime isn’t working harder. They’re operating in a structurally different world.

Two curves. Same starting point. The difference isn’t rate. It’s regime. One adds value at a constant pace. The other applies each improvement to the next round of improvement. When the compounding curve separates it moves into a different frame. Some people and some organizations end up in that second curve. The decisions we are making today about how to engage machine intelligence systems project forward with compounding impact. Two key dimensions may be strong path predictors.

Curiosity Depth and Application Breadth Differentiate Outcomes

Before mapping the space with cleaving factors, we need two working definitions.

Substrate engagement describes how deep into the mechanism a person is willing to go. It tends to be high when someone asks “how does this work?” rather than stopping at “does this work?” It tends to be low when a product’s description fully bounds someone’s imagination of its use. Surface engagement means using the tool as packaged. Substrate engagement means treating the tool as a capability surface: something that can be examined, recombined, extended, or used as input to building something else.

Fleet leverage describes the scope of the capability a person creates. It tends to be high when the output of someone’s work raises the ceiling for many others. That looks like workflows, systems, shared infrastructure, and patterns that everyone downstream can use. It tends to be low when the advantage stays personal and local, whether by design or by default.

These two dimensions are independent. You can go deep without sharing. You can share without going deep.

The Archetypes

Mapping these dimensions generates four recognizable modes. This is reductive. It might also be useful.

Executors engage at the surface and generate local output. They use available tools as presented, solve immediate tasks reliably, and optimize within the current workflow. This isn’t a criticism. Executors are the operational backbone of any functioning organization. Reliable execution has real value. The risk is that it has a ceiling: the ceiling of the tools available in their current form, used in their current configuration.

Evangelists stay at the surface but build for the fleet. They spread successful usage patterns, build playbooks and prompt libraries, document workflows and share them broadly. Where Executors run the current plays, Evangelists raise the baseline across the team. Their leverage is real, though bounded by the depth of the usage layer they’re operating in.

Craftsmen go substrate-deep but keep it local. They descend into the mechanism, explore primitives, and build custom tools for themselves. They often generate spectacular personal leverage. The risk is that the capability stays private, either because they never thought to share it, because the incentive structure doesn’t reward sharing, or because obscure advantage feels like a feature rather than a bug. High individual output, low fleet multiplier.

Multipliers are the third curve. They go deep and they build for the fleet. They use tools to make better tools, and they make those better tools available to others. They work in the open. They convert insight into shared infrastructure. They treat a clever personal workflow as the first draft of a capability that should belong to everyone. The advantage they generate is genuinely compounding because each improvement becomes the base to which all future improvements apply, across many people rather than one.

Multipliers are rare. Most human capital performance systems are not designed to optimize their contribution because those systems evolved from industrial production functions with low dynamism and highly layered command and control systems. They were not designed to inspire entrepreneurialism.

Where Does the Orientation Come From?

The natural question is whether this is a skills difference or something else. It isn’t primarily a skills difference.

The archetypes above are behavioral expressions of something more fundamental: how rewarding a person finds the act of exploring mechanism, and what kind of environment they’re in when they do it. Both dimensions matter. Neither is fully fixed.

The model here has two underlying drivers. The first is reward profile, specifically, what feels intrinsically motivating: completion and output (finishing tasks), or exploration and mechanism (understanding how things work). Researchers who study this draw on a body of work around epistemic curiosity and intrinsic motivation that traces back at least to Deci and Ryan’s self-determination theory. The short version: some people experience a gap in understanding as rewarding in itself. Others experience it as friction to be resolved as quickly as possible so they can get back to the work. Neither is wrong. They’re different motivational architectures.

The second driver is normative environment: culture, incentives, identity, and permission structures. Whether someone’s curiosity about mechanism gets activated often depends on whether the environment licenses it. Organizations that punish tinkering, penalize broken prototypes, and reward only visible throughput are effectively telling the people with substrate-seeking reward profiles to stop doing the thing that would make the organization most valuable. This happens constantly. It’s not malicious. It’s the predictable output of incentive systems optimized for short-term execution rather than long-term capability creation.

This matters for a reason the framework diagram can’t show: you don’t need to find fully formed Multipliers. You need to create conditions where people with the latent orientation can discover it in themselves. The disposition is more common than the behavior. The suppression is usually environmental.

The archetypes in this map aren’t a skills hierarchy. A Non-prioritizer isn’t deficient. They may simply be in the wrong context for this behavior to matter or activate. A Utilizer isn’t failing to be a Toolmaker. The categories describe a relationship between a person’s motivational profile and a particular product or tool in a particular environment. Change the environment, and some of those categories shift.

One more structural note: the failure response is a modifier, not a peer archetype. Whether someone stops or tries again after friction intersects with all of these categories. A Curious person can become a Skeptic if engagement through friction is punished enough times. A Reapplicator can become Resilient if the culture treats failed experiments as information rather than embarrassment. Persistence under friction is partly individual disposition, partly organizational design.

Things Are Very Different Now

For most of the history of toolmaking, the threshold between using a tool and making one required a significant investment. You needed either deep technical knowledge, meaningful slack time, or both. The path from “I want a better workflow” to “I have a better workflow” was long enough that most people rationally deferred. They waited for a product person somewhere to identify the gap and close it. Machine intelligence has meaningfully lowered that threshold.

This is not the standard “AI makes you faster” claim. The more precise version is: machine intelligence lets people work closer to the material, in the sense that Ryo Lu describes when he contrasts designing in static mocks versus designing in code. The real material of software has always been code, not mockups. The real material of knowledge work has always been structure, logic, and architecture, not finished documents. But operating at the level of material required skills that took years to develop. Now those skills are increasingly available on demand.

What that means for the Compounding Advantage Framework is this: the distance between Executor and Craftsman is shrinking. The gap between “use the tool” and “build a better tool” is lower than it has ever been. People with substrate-seeking reward profiles who previously lacked the technical capability to act on that instinct now have a path.

The old stack (language → runtime → components → operating system → platform → tools → products) was hierarchical. Each layer required significant time investment to master a layer. The new stack is increasingly reproductive: products can generate tools, tools can generate workflows, workflows can generate new products. Moving across layers is faster. This benefits substrate-engaged people disproportionately, because the exploration they were always inclined to do now costs less to pursue.

Not everyone will become a Multiplier, but the fraction of people who can shift into that mode is much larger than organizations typically plan for. And the opportunity cost of not enabling that shift has gone up, because the potential benefit from each Multiplier (measured in fleet leverage and compounding capability) has also gone up with tool creation availability.

Growing Capacity Requires Incentive and Culture Design

Most organizations optimize for run work because this is how they stay afloat. Coordinating and improving work inside an existing possibility set is the hallmark of well run business.

Organizations that optimize exclusively for run work do not engage the frontier that delivers growth. They extract value from the tools they have, seeking capacity rather than capability, until those tools are incapable of delivering the required value growth required. Then they scramble to acquire the growth capability they should have invested in earlier and now need to scale. They over-reward reliable execution and under-recognize the people who change what the system can do next. The cleaving factor here is not technical fluency. It’s whether a person’s work expands the possibility set or operates within it.

Executors and Craftsmen can both operate inside the boundary. The Executor runs the current play. The Craftsman optimizes their personal version of it. Neither changes what the organization can do next. Evangelists and Multipliers both operate at the boundary, but in different ways. The Evangelist lifts the floor, spreading capability that exists. The Multiplier pushes the ceiling, creating capability that didn’t.

Roger Martin has written about organizations as decision factories. In that framing human time, attention, and cognition are the scarce inputs. Better tools make work faster work and preserve scarce human cognition for the work closest to the frontier: classification, judgment, experimentation, and capability creation. Each time a Multiplier converts a clever workflow into shared infrastructure, they’re not just saving time. They’re reallocating the cognitive budget of everyone downstream toward other problems. That’s the compounding mechanism, applied to cognition.

OpenClaw is a rapid prototyping tool, for tools!

OpenClaw is giving us a punctuated experiment of what happens when we give people the ability to build tools, that can build tools.

When people are given a system that can do almost anything, the variation in how they use it is diagnostic. Some solve local tasks. Some create reusable workflows. Some build systems that raise the capability of others. Some hoard advantage and obscure their methods. Some work in the open and treat every clever technique as a contribution to the shared fleet.

None of this is determined by the tool. The tool is neutral with respect to these orientations. What determines it is the interaction of individual reward profile and organizational incentive structure, the same two variables at the base of the relationships-with-products model.

This is why OpenClaw matters as an observation, not just a product. When you distribute generalized agency, you get a natural experiment in organizational culture. The distribution of behaviors you observe isn’t a measure of individual talent. It’s a measure of what your incentive system actually rewards, versus what it says it rewards.

Organizations that want Multipliers need to make fleet leverage visible. They need to reward people not just for throughput, but for the capability they create in others. They need to treat “I built a tool that made ten colleagues faster” as organizationally more significant than “I completed ten tasks.” Currently, most performance systems can’t measure the first thing at all.

Legacy Talent Systems Do Not Multiply Multipliers

Hiring for multipliers is possible but requires different signals. Multipliers don’t primarily show up on resumes as high performers, but Executors who grind existing processes do. Multipliers are people who spontaneously built something to share, who sent the playbook before anyone asked, who treated a good personal solution as the beginning of a shared one. The interview question “tell me about a time you improved a process” is appropriate for a static system context where optimization is sufficient. I prefer “tell me about something you built that other people used.” The answer tells you which quadrant someone lives in.

Developing it requires permission more than training. The capability for substrate engagement is latent in more people than organizations realize. What it needs is license: explicit permission to descend into mechanism, tolerance for broken prototypes, and an incentive structure that treats shared capability creation as strategic work rather than a distraction from real deliverables. This is not about starting an innovation program. It’s about removing the signals that tell substrate-seeking people to stop.

Working in the open is not a personality preference. It’s the mechanism. A Craftsman who goes deep and keeps it local produces excellent personal output. But the organization gets no compounding from that behavior. The transition from Craftsman to Multiplier isn’t primarily about capability. It’s about the decision to treat personal leverage as the prototype for fleet leverage. That decision is shaped by whether the environment makes sharing safe and rewarding.

The Craftsman-to-Multiplier transition is the highest-leverage talent intervention available. You already have the people. The question is whether the system is set up to convert their personal mastery into shared capability, or whether it creates incentives to hoard it.

“Compound/Interest” was not a typo

With the tools available now we have the opportunity to deliver compounding value for our organizations if we spark the interest of people with the ability to bend the curve of our work. The leadership opportunity we have now can radically alter the future returns available in ways we have never seen. In periods of rapid change, when the tools are evolving faster than the documented playbooks, unleashing the Multipliers determines whether an organization extracts the expected value from its capabilities, or the unexpected value of capabilities it didn’t know it could have. The difference between those two outcomes is structural. Understanding it is a starting point. Building for it is the work of leaders, right now.

Management and leadership are both systems of action that involve deciding what needs to be done, creating networks of people and relationships that can address necessary objectives, and making sure the work is done. But they are different activities.

Defining the terms “leadership” and “management” helps use these terms with greater precision. We can use this precision to set appropriate expectations for what good leadership looks like. We draw substantively from John Kotter’s Harvard Business Review article “What Leaders Really Do” to extract concepts that can be used for an analytic framework that focuses on:

· perceiving what is coming;

· understanding interdependencies;

· setting vision;

· effective Communication;

· aligning stakeholders;

· earning credibility;

· inspiring and motivating;

· developing networks; and

· coaching.

We Lead Organizational Change

Leadership focuses on strategic capability. Support that helps our organization adapt for the things we do next. Effectiveness is sustained effort that produces experimentation and learning. What elements describe the work and outcomes of leadership?

· Helping organizations change.

· An inductive process focused on setting direction (vision and mission) and necessary change (strategy) to thrive in a future that differs from the current state. What’s next?

· The work is communicating (receiving and transmitting) and collaborating to analyze trends and synthesize a new approach to adapt to thrive in a future that is dissimilar to the current state.

· Capacity is built upon aligning people who can cascade new direction through the organization so it absorbs and commits to achieving the new strategy.

· Objectives are accomplished with motivation and inspiration that inspire people to keep moving in the right direction and adapt to obstacles.

· Leading change may be expressing what is already well known in a way that inspires people to act because it serves the interests of important constituencies and builds upon strengths and capabilities that can deliver reasonable outcomes.

· Understanding interdependence among uncontrolled/autonomous actors and shaping their collective action with alignment built upon communication.

· Authority comes from demonstrated credibility and is sustained by belief built upon delivered empowerment.

· Motivation is based upon energization that offers achievement.

· Correction is coaching that focuses on direction of outcomes.

· Builds upon informal networks and environments that require high trust for performance.

We Manage Organizational Complexity

Management focuses on operational capability. Support that helps our organization do things we do now. Effectiveness is sustained effort that produces optimization and efficiency. What elements describe the work and outcomes of management?

· Helping organizations manage complexity.

· A deductive process focused on creating order and consistency. What’s missing?

· The work is planning (setting goals and targets), creating actionable steps (operational controls), and budgeting (allocating resources) to adapt to thrive in a future that is like the current state.

· Capacity is built upon creating organizational structure, defining roles, staffing with appropriate talent, communicating the plan, delegating accountabilities, monitoring progress, and correcting performance.

· Objectives are accomplished with control and problem solving that identifies and removes barriers to plan.

· Understanding the features of work delivery among controlled actors and organizing their collective action with structures (incentives, roles, and reporting relationships).

· Authority comes from the role and is sustained by delivering on plan.

· Motivation is based upon compliance that offers safety.

· Correction is instruction that focuses on quality of processes.

· Builds upon formal networks and environments that do not require high trust for performance.

Appendix A-Concept Comparison of Analysis, Management, and Leadership

Business direction builds upon several complementary capabilities that apply different skillsets to advance different phases of the lifecycle of work in dynamic business environments. The figure below summarizes the high-level conceptual objectives of those different phases to compare the different work of analysis, management, and leadership.

Appendix B-Task, Patterns, and Systems Impact Progression Model

I frequently tell a joke that is not a joke.

What is an attorney’s favorite application?

People usually bid Microsoft Word, Outlook, or a research tool. I respond with

“A secretary!”

The most valuable “application” in an attorney’s working life is not a tool they operate directly. It is a system that observes their work, infers intent, acts autonomously within understood bounds, learns preferences over time, and shields them from the operational complexity of other systems. In other words, a system of action.

Much of the current conversation about AI products focuses on capability. What can the system do, how broadly can it generalize, and how quickly is that frontier advancing. These questions matter, but they often obscure a more fundamental constraint in professional environments: human cognition.

Unallocated time, attention, and cognitive effort are increasingly scarce. Many AI products impose a high fixed cost before they deliver meaningful value. They require users to learn new mental models, new workflows, and new modes of interaction. Even when a tool is extremely powerful, the effort required to decide what to ask, how to ask it, and how to evaluate the output can outweigh the benefit.

For many people, a general purpose interface still feels like a command line. Capable of almost anything, but demanding fluency, experimentation, and sustained attention. That is not how most workdays are structured.

Most people’s days are dominated by very specific somethings. Deadlines. Drafts. Emails. Scheduling. Follow ups. Coordination across people and systems. The relevant question is not whether a general system can handle these tasks in principle. It is how people want those tasks to be handled, and how much thinking they are willing to spend orchestrating the handling.

This is where the secretary becomes a useful prototype rather than a nostalgic metaphor.

One reason this pattern is so consistently missed is experiential. Many people building productivity technology have never worked as a secretary or closely alongside a great one. They have not lived the work of observing, inferring, buffering, and optimizing on behalf of a principal. As a result, products are often designed around explicit tasks and interfaces rather than around the gradual accumulation of optimizations that reduce cognitive effort over time.

A great secretary does more than execute explicit instructions. They observe a principal’s work and begin to recognize patterns. When confidence is high, they act without being asked, taking a task as far as their authority and capability allow. They execute asynchronously and revert only when more input is required. Over time, they learn preferences that are rarely written down. Formatting quirks. Communication styles. Risk tolerances. These preferences become implicit knowledge that is costly to recreate and therefore highly sticky.

Secretaries also act as a shim between the principal and a long tail of tools and systems. Time and billing. Travel booking. Filing systems. Internal workflows. The principal does not need to master these systems because the cognitive and operational burden has been delegated. As a result, value accrues to the system of action, not to the underlying systems of record.

Seen this way, the secretary is not just support staff. It is an existence proof. A demonstration that the most durable productivity gains come from systems that conserve cognition, learn continuously, and earn the right to act on a user’s behalf.

If human cognition is the constraint, then the central design question for AI products is not how general they are, but how effectively they function as systems of action. That reframes the platform versus product debate and suggests that many of the things great secretaries do are worth our time, attention, and cognition. They are the prototype for experiences that make us more effective.

“Efficiency is concerned with doing things right. Effectiveness is doing the right things.”
-Peter Drucker

Most companies think AI will make them smarter by producing more insight. That might only be half of the story. What if unbalanced AI adoption creates a denial of service attack on your decision infrastructure?

Roger Martin has described knowledge work organizations as decision factories. They take messy inputs and ship decisions as output. Meetings are the production line. Decks and memos are work-in-process. Rework looks like “let’s revisit this next week.”

AI dramatically lowers the cost of producing insight, which means we will ask for far more of it. The factory will fill with analysis.

Here is the operational risk: if you increase the throughput of one stage of a factory without increasing the throughput of the downstream stages, work-in-process piles up, coordination costs rise, and cycle time slows down.

That is the denial-of-service pattern. Not external. Self-inflicted.

For this post, I am using “insight” to mean any unit of analysis that could change a decision. A scenario run, a risk flag, a comparison table, a recommendation, a draft, a forecast.

AI could make many firms slower, not faster, unless they invest in insight consumption and systems of action as aggressively as they invest in insight production.

The decision factory, decoded

A helpful way to decompose most leadership decisions is:

• Should we do this? Strategy

• Can we do this? Constraints

• How do we do this? Execution

Insights power each step, but in different ways. Strategy needs framing and tradeoffs. Constraints need validation and routing to the right experts. Execution needs translation into work, owners, and feedback loops.

AI changes the economics of generating the insights for all three steps. That is not the same as improving the decision factory’s ability to turn those insights into decisions and actions.

The rest of this post is a model that tries to make that mismatch visible.

Figure 1: Production gets cheap, fast

In the first chart, I isolate the production side: cost per insight produced as experience grows.

Two ideas matter:

Human-only production has a smiling curve. It is expensive at cold start because everything is bespoke. It gets cheaper as people learn the domain and reuse patterns. Then it gets expensive again at scale, as cognitive capacity and coordination limits re-assert.

Human plus AI production starts more expensive because there are upfront investment costs: design, tooling, workflow integration, evaluation, and governance. But then it falls sharply with scale and trends toward a much lower long-run cost because machines have near-infinite capacity for analysis and coordination, and the system amortizes fixed costs.

This is where most AI-forward planning lives today: how do we get the production curve down.

Figure 2: Consumption does not behave like production

The second chart isolates the consumption side: cost per insight consumed as experience grows.

This is the side most organizations do not model.

Human-only consumption has a hard limit. At some point, the cost of consuming insights rises rapidly, not because humans get worse, but because the system asks them to do more triage, more validation, more alignment, and more coordination per marginal unit of output.

This is what the “absorption wall” looks like in practice: more analysis creates more managerial work, which creates more coordination, which creates more delay, which creates more rework.

Human plus AI consumption can remain viable longer because machines can shoulder triage, compression, routing, and parts of verification. The result is not “humans removed,” but a consumption process that does not collapse under volume.

This chart forces an uncomfortable question: are we building a production engine that our organization cannot absorb?

Figure 3: The composite reveals the breakover mismatch

The composite chart overlays production and consumption and highlights the regimes leaders should care about.

On the consumption side:

• From low scale to the intersection, the gap represents the investment cost of deploying AI to consume insights.

• From that intersection to the human-only consumption asymptote, the gap represents the increasing cost of consuming insights using only human leverage.

• Beyond the asymptote, the area under the human plus AI curve represents opportunity of scale inaccessible without human plus AI. In practice, this is where the decision factory cannot keep up without machine-mediated consumption.

On the production side:

• From low scale to the intersection, the gap represents the investment cost of deploying AI to produce insights.

• Beyond the intersection, the gap represents the increasing cost of producing insights using only human leverage.

The key feature is the highlighted region: the breakover points for production and consumption do not have to line up.

If insight production becomes cheap before insight consumption capacity scales, the decision factory gets flooded. Outputs accumulate faster than humans can validate, align, and act. Work-in-process grows, coordination costs spike, and cycle time lengthens.

This is the internal denial of service pattern. It happens when the production breakover point sits to the left of the consumption breakover point.

Why this matters: planning for production without planning for consumption

Most firms treat AI as a production upgrade: more analysis, faster drafts, more scenario generation, more monitoring.

Wright-style learning makes production cheaper with repetition and tooling. Then Jevons kicks in: cheaper insight increases demand for insight. Teams ask more questions because they can.

Without deliberate redesign, the decision factory fills with work-in-process. You get:

• more analysis that does not resolve uncertainty

• more options that increase perceived risk

• more stakeholders requesting more evidence

• more meetings

• more rework

• slower decisions that feel more rigorous

This is the failure mode of the AI-forward firm that invests only in production.

The leadership move: build systems of action

If you want an AI-forward decision factory, you need a system of action that converts insight into decisions and decisions into changes in the world.

Most enterprises have systems of record (where facts live) and systems of insight (where analysis is displayed). What they lack is a system of action: orchestration that routes work, gates decisions, binds outputs to owners, and drives execution.

This is mostly consumption infrastructure.

Here are practical design moves that map cleanly to the three-step decision decomposition:

Should we do this?

• Standard decision artifact with explicit tradeoffs

• Evidence standards that clarify what would change the answer

• Timeboxed decision windows

• Decision rights that are explicit, so “alignment” does not become an endless meeting series

Can we do this?

• Constraint routing workflows instead of meetings

• Clear pass/fail criteria for legal, security, finance, capacity, brand

• Provenance and auditability for high-risk constraints

• A default policy for what gets escalated to humans versus handled by workflow

How do we do this?

• Action compilation: owners, milestones, budgets, operating metrics

• Automated handoffs into the execution systems (tickets, docs, comms, approvals)

• Feedback loops tied to outcomes so new signals update the decision rather than spawn new decks

This is an absorption and execution strategy. It is how you keep insight supply from turning into coordination load.

The capital allocator move: invest where the bottleneck lives

Most investment flows to production because it is easy to demo. But durable advantage will come from consumption and action capacity:

• triage and ranking

• verification and provenance

• orchestration and routing

• auditability and governance

• integrations that bind decisions to execution

The winners will not be the firms that generate the most insight. The winners will be the firms that convert insight into coordinated action with low rework.

Systems of action orchestrate work for scale

AI lowers the cost of producing insights. Organizations respond by demanding more insights. If consumption capacity does not scale, the decision factory slows down.

The opportunity is to redesign the factory so insight abundance translates into decision throughput.

That requires building systems of action that extend systems of record and insight. These complement the single-player AI tools we are putting in our people’s hands and help us orchestrate their work. This ensures the outputs of those tools accelerate our factories and avoids accidental denial of service attacks from the inside.

This is my attempt to make career choices less mystical. Instead of arguing about whether a job is “good” or “bad,” I break the job down into the three deals it is actually offering you and then ask a more precise question: which deal am I optimizing right now, and what am I paying for it?

This model applies Cleaving Factors.

Smuckwell Industries

Cleaving Factors

My friend Jae introduced me to a concept she calls a “cleaving factor”. I look forward to her correcting my understanding of a cleaving factor as a simplified model, of a complex space, that explains or predicts a system response, using heuristics, for scenarios w…

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4 months ago · 2 likes · Jason Barnwell

The premise

A job is not one thing. It is a bundle of three simultaneous bargains:

• Security: How much the role stabilizes life and reduces downside risk.

• Status: How much the role confers leverage, access, and narrative power.

• Experience: How the work feels day to day, including growth, agency, and meaning.

Most frustration comes from one of these being quietly “off” while the others look great.

The model in one sentence

The Job Deal framework models a role as a point in a 3D space (Security, Status, Experience), and uses three linked 2x2 maps to make that space easy to talk about.

Those three 2x2s are not separate ideas. They are projections of the same underlying 3-space.

The three axes (plain language, operational definitions)

Security

Security is runway and predictability. It is the degree to which your job makes life easier to plan.

Security tends to be high when:

• you can absorb a disruption without panic

• pay and hours are stable

• benefits and protections are real

• you could land a comparable role without a heroic scramble

Security tends to be low when:

• you are sponsor dependent or budget fragile

• income or hours are volatile

• a reorg or funding event can wipe you out

• re-employment is slow or uncertain

Status

Status is narrative leverage. It is not about ego. It is about access and convertibility.

Status tends to be high when:

• your title or brand opens doors without explanation

• people seek your input early

• you can move resources or decisions

• you have a platform (internal or external)

Status tends to be low when:

• you are interchangeable

• your work is local and invisible

• your voice is optional

Experience

Experience is the day-to-day felt reality of the work.

Experience tends to be high when:

• you are learning and compounding a craft you care about

• you have agency over how the work is done

• the people around you are competent and respectful

• the work has coherent meaning

• you are not chronically depleted

Experience tends to be low when:

• you are drained, stagnant, or cynical

• your calendar is dominated by intake and rework

• you have little agency

• the environment is chaotic or political in the worst sense

The first map: Security vs Experience

This is the simplest bargain: stability versus how the work feels and compounds.

The four modes:

• Adventurer (high experience, low security): learning and meaning, but thin runway and volatility.

• Craftsman (high experience, high security): sustainable mastery with stable footing.

• Grinder (low experience, high security): predictable life funding, but the work itself is a slog.

• Drifter (low experience, low security): reactive work, little compounding, and fragile footing.

If I say “I wanted a new adventure,” I am usually saying: I was willing to trade some Security for higher Experience, at least for a while.

The second map: Status vs Experience

This is the bargain between craft and visibility. It is where many people accidentally drift.

The four modes:

• Tinkerer (high experience, low status): deep craft, low visibility, low leverage.

• Icon (high experience, high status): craft plus platform, credible influence.

• Politician (low experience, high status): coalition and narrative work dominates; craft joy often declines.

• Cog (low experience, low status): queue work with low agency and limited recognition.

This map explains a lot of the “I’m successful but unhappy” phenomenon. Status can rise while Experience quietly decays.

The third map: Security vs Status

This is the bargain between institutional footing and prestige. It is the leverage map.

The four modes:

• Hotshot (high status, low security): big upside, sponsor dependence, fragile footing.

• Pillar (high status, high security): institutional trust, durable influence.

• Lifers (high security, low status): stable and durable, low visibility.

• Marginal (low security, low status): exposed, low bargaining power.

This map is useful because many “glamorous” roles are actually Hotshot roles. They can be amazing. They are also easy to misprice.

Putting it together: the 3-space view

When you put Security, Status, and Experience together, you get a 3D space. Each job is a coordinate: (S, P, E).

I use two levels of description:

• Continuous scores (0–10) for nuance.

• High/Low states for fast conversation and quadrant naming.

Those High/Low states create eight “corner” job deals. I name them to describe the role, not the person:

• Sweet Spot (H,H,H): meaningful work, real influence, stable footing.

• Court (H,H,L): protected influence, low joy, coordination heavy.

• Craft Haven (H,L,H): stable craft, low politics, low leverage.

• Safe Seat (H,L,L): predictable, low stakes, draining.

• Rocket (L,H,H): high meaning and visibility, fragile footing.

• Glass Throne (L,H,L): visible but unstable and unfun.

• Indie Lane (L,L,H): good work, exposed and under-recognized.

• Exposed Corner (L,L,L): low leverage across the board.

You do not need to live at a corner to benefit from the map. Corners just make the shape of the space easy to understand.

Why I split the axes the way I do

If you use simple midpoint thresholds (5/10), labels can flip week to week. Real jobs do not change that fast.

So I treat High/Low as a stable state with a center line (often around 6) that reflects hysteresis: once you are “in” a state, you need more evidence to flip out of it.

In practice, this means:

• labels are stable

• movement needs to persist

• you do not relabel your life based on a single good or bad week

The practical question: which axis is the binding constraint?

A useful rule:

1. If Security is low or volatile, fix that first. Low security taxes everything.

2. Else if Experience is low, fix that next. Low experience corrodes sustainability.

3. Else, if you are blocked from impact by access or resources, invest in Status.

This is not morality. It is constraint management.

Canonical moves (how people actually change jobs)

Most career narratives are a small set of moves repeated in different clothing.

Recraft (raise Experience without blowing up Security)

• Example: Grinder → Craftsman

• Price: boundaries and political capital

• Mechanism: shift from intake to ownership, protect maker time, increase learning rate

Make it legible (raise Status while preserving Experience)

• Example: Tinkerer → Icon

• Price: visibility tax

• Mechanism: publish artifacts, take visible ownership, build a platform tied to outcomes

Institutionalize (convert Status into durable Security)

• Example: Hotshot → Pillar

• Price: less upside convexity, more constraints

• Mechanism: diversify sponsors, formalize decision rights, attach to durable systems

Stabilize (raise Security first)

• Example: Drifter → Grinder

• Price: less novelty for a period

• Mechanism: horizon, predictability, and downside coverage

The main failure mode: drift

If you do not name the trade, the trade happens anyway.

Three common drifts:

• Icon → Politician: visibility tax consumes craft time.

• Craftsman → Grinder: stagnation or overload erodes experience.

• Hotshot → Marginal: sponsor dependence breaks and status collapses.

The antidote is operational guardrails:

• maker time floors

• meeting caps

• artifact cadence tied to outcomes

• sponsor diversification

• runway thresholds

• learning cadence

A closing note on “adventure”

Adventure is not reckless. It is an explicit trade: more growth and meaning, paid for with more variance and less predictability. Sometimes that is the right move.

The point of this framework is not to push anyone toward one corner of the space. It is to help you make the trade consciously, and to give you language that is more accurate than “I just needed a change.”

My friend Jae introduced me to a concept she calls a “cleaving factor”. I look forward to her correcting my understanding of a cleaving factor as a simplified model, of a complex space1, that explains or predicts a system response, using heuristics, for scenarios with adequate pattern strength to be valuable. I have been applying these to my practice without realizing it. For example, I frequently produce 2 x 2 plots and other models that help me understand phenomena that impact my professional work.

I will share some plots I use to help me think going forward. This entry is an explanatory reference of their utility. I will explain why I need them before I further decode cleaving factors.

My mind is inadequate. I do not have enough thinking resources to do all the things I want it to do. I have working memory, long-term memory, thinking speed, information input rate, and other constraints that limit my throughput (thinking per unit time) and total space (amount of information in thinking). A way to address these constraints is to spread thinking over more time and condense information into lower fidelity chunks that remain useful.

Strategic precognition creates reusable assets. Making and sharing valuable information chunks creates value for makers and the recipients. The value to information makers of engaging the distillation process is probably understated. It may be easier to observe and assess producers sharing distilled knowledge with recipients than applying it to their own work. This obscures why activities like storytelling and writing are a necessary part of generating valuable heuristics that serve the producer.

Production develops special expertise. Producers retain a fluency with heuristics they make that is harder to share. This lets them apply and remix distilled intelligence to exceed their instant capacity. Recipients may become fluent, or they may be effective mimics. We find out if recipients have mastered a heuristic when the problems become big, fast, and complex. I make cleaving factors because I seek work that exceeds my instant thinking capacity and requires me to become fluent with tools that do not exist or are not shared.

This pattern is AI-forward. People who access this metacognitive process of reducing implicit heuristics to declared practices will be more effective working with machine intelligence. The distillation process pushes us to declare objectives (desired outcomes), value function (paths that yield more utility), boundaries (constraints on approach), and context (shaping information including existing knowledge and starting state). Providing these inputs to machine intelligence operations translated for the local operating context turns AI into more accurate and precise tools.

A proposed cleaving factor definition.

• simplified model: turns a complex problem into something with fewer elements, accepting some loss of detail in exchange for ease of use and speed.

• of a complex space: applies to situations with many interacting variables, paths, or outcomes, where reasoning directly about the whole system would be slow or impractical.

• explains or predicts a system response: helps you understand how the system behaves or anticipate what is likely to happen when certain conditions are present.

• using heuristics: relies on simple decision criteria supported by observations rather than exhaustive analysis or precise calculation.

• for scenarios with adequate pattern strength: is only applied where the patterns are consistent enough that the simplification holds and produces reliable guidance.

• to be valuable: delivers more practical utility than a more precise but harder-to-use model, especially under time, attention, or cognitive constraints.

Cleaving factors are powerful because they push us to focus on the features that are most relevant in the current problem space. This creates models that are simple and powerful when they are applied to the appropriate context. Correct application requires discrimination and discipline. Select a useful cleaving factor. Apply that to the appropriate regime of the total context.

A starting place is to identify all of the features that are relevant to describe a scenario of interest. Figure out the ones that seem to matter most because they correlate with a useful state or outcome. Then start mixing and matching those features with the states and outcomes to see what coalesces and expresses valuable relationships. This is easier than it sounds. The animation below summarizes the conceptual discrimination process.

Let’s make this more concrete by imagining you are planning a multi-day adventure trek, in snowy mountains, during the winter. There are many features of material preparedness you could observe for this activity. To simplify the analysis to understand features that combine to predict interesting outcomes, pick a couple of the most prominent features and see how they combine.

We can plot these features as two axes with high and low values. We can then segment the plot into quadrants that attach an archetypal outcome or state that commonly results from the intersection based upon our observations. We are starting to declare heuristics with cleaving factors as discriminants. As we make and share these heuristics we train ourselves to pay attention to these cleaving factors based upon pattern similarity.

You can also adjust the valence of cleaving factors by grouping features to create synthetic cleaving factors. We commonly hear people talking about discussing things “at the right altitude”. That is adjusting the level of abstraction for the underlying details to the common level of understanding for the audience so everyone can participate. Adjusting cleaving factor valence is similar.

With this view we see that features can have improved predictive and explanatory power when they are grouped elegantly. This elegance benefits from wisdom and experience. We refine groupings over time as we see clusters produce stronger correlations between patterns and outcomes that impact decisions.

We can use synthetic features to create a new 2 x 2 that expresses the patterns at a different altitude.

This is a fanciful example, but I hope it highlights a notion: understanding how information connects to create simpler models that are easier to use and retain fidelity is art that might come from experience. A challenge when making these tools for others is tuning them for your audience. Everyone brings different experiences with them and their desire and willingness to decode varies.

I look forward to sharing visualizations that present cleaving factors I find interesting. The example below is one I have been thinking about.

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Adapting to an increasingly complex (intricate) and complicated (interconnected) business landscape requires us to structure teams that can simultaneously drive innovation, maintain operational excellence, and adapt to changing market conditions. Linda Hill expresses this as “moving beyond either-or thinking to both-and thinking”. The framework below identifies leader archetypes within this evolutionary context by plotting leader characteristics on exploration-exploitation and systematic-adaptive axes. These archetypes provide models for where and how we deploy these strengths to deliver success. This is reductive. It might also be useful.

The Leadership Archetype Quadrants

Presume four distinct leadership archetypes emerge from the intersection of two dimensions: the leader’s orientation for new or existing value (exploration vs. optimization) and the leaders approach for finding it (systematic vs. adaptive). Each archetype has strengths, challenges, and optimal contexts for deployment.

Systematic-Adaptive vs. Exploration-Optimization

We can map the leadership archetypes onto our generalized systematic-adaptive and exploration-optimization axes.

Figure 1: The Leadership Archetype Quadrant - Illustrating the relationship between systematic/adaptive approaches and exploration/optimization orientations.

Scouts: The Methodical Explorers

Positioned in the systematic-exploration quadrant, Scouts excel at comprehensive analysis and structured investigation. With their analytical mindset, they meticulously map landscapes before committing resources. They’re invaluable for research-intensive initiatives, strategic planning, and risk assessment. Their systematic approach ensures no stone remains unturned, though they may struggle with rapid decision-making.

Hunters: The Agile Opportunists

In the adaptive-exploration quadrant, Hunters thrive on identifying and pursuing emerging opportunities. Bold and curious, they excel in uncertain environments that require quick pivoting and experimentation. These leaders drive breakthrough innovation and market expansion but may create instability when placed in roles requiring consistent execution.

Farmers: The Disciplined Optimizers

Occupying the systematic-optimization quadrant, Farmers excel at creating predictable, incremental value through methodical processes. They build and maintain stable operations, establishing reliable systems that deliver consistent results. While exceptionally effective at efficiency improvements, they may resist necessary disruption during periods of significant change.

Gardeners: The Responsive Enhancers

In the adaptive-optimization quadrant, Gardeners cultivate continuous improvement while maintaining flexibility. They balance stability with responsiveness, adapting established processes to evolving conditions. These agile leaders excel in change management and organizational development, creating environments where improvements flourish organically.

Mapping Archetypes to Problem-Pattern Frameworks

We can also map the archetypes onto developed problem-pattern frameworks like the The Rumsfeld Matrix and the Cynefin Framework. These provide other ways the leader model can be applied to problem-type domains and problem-solving contexts.

Mapping Leadership Archetypes onto the Rumsfeld Matrix

The Rumsfeld Matrix focuses on if something is known to exist (awareness) and if its nature is discerned (understanding). We can map into this matrix directly.

Figure 2: The Rumsfeld Matrix of awareness (knowledge something exists) and understanding (knowledge of what to do).

Mapping Leadership Archetypes onto the Cynefin Framework

Cynefin focuses on the nature of ordered and disordered problems. Cynefin’s practice sequence recommendations (i.e., the sequence of act-categorize-analyze-probe-respond-sense) applied to clear, complicated, complex, and chaotic domains offer pattern convergence with the archetypes. Some may argue that Scouts and Gardeners should be inverted here, and I am open to that. My approach puts experiments in the domain of Gardeners, and that is typically how you work through the complex.

Figure 3: The Cynefin Framework (Clear, Complicated, Complex, and Chaotic) expressed in quadrant form.

Archetype Recognition: Popular Culture Examples

The archetypal abstractions are frequently expressed in popular culture. These concrete representations can make it easier to identify, understand, and engage the archetype. The specific classification examples are very debatable.

Figure 4: Leadership archetype examples from popular culture.

Archetypes in Value Creation: Different Phases Need Different Leadership

The value development lifecycle of a product or business follows a common arc. The archetypes have different capacities to contribute at different stages in the journey because their strengths are aligned to specific phases of development.

Figure 5: Leadership archetypes and their roles in the progression from idea to established value.

As illustrated in the figure above, the value creation journey can be understood as a progression through distinct phases:

1. Discovery Phase (Hunters): Hunters identify new opportunities and possibilities in uncharted territory. They thrive in environments of high uncertainty and potential, spotting patterns and connections others miss. Their adaptive exploration mindset allows them to rapidly identify promising directions without getting bogged down in excessive analysis.

2. Validation Phase (Scouts): Once potential opportunities are identified, Scouts systematically explore and validate their viability. They bring methodical rigor to understanding the landscape, conducting structured research, analyzing market data, and building comprehensive maps of the opportunity space. Their systematic approach ensures thorough validation before significant resources are committed.

3. Scaling Phase (Gardeners): As validated ideas move toward implementation, Gardeners excel at adaptive optimization—building flexible systems that can respond to evolving conditions while maintaining core functionality. They create adaptable frameworks that balance structure with flexibility, allowing innovations to grow in controlled but responsive environments.

4. Optimization Phase (Farmers): When solutions reach maturity, Farmers apply systematic optimization to maximize efficiency, reliability, and predictable value creation. They establish standardized processes, implement quality controls, and drive continuous refinement to extract maximum value from established systems.

This progression illustrates why organizations need all four archetypes working in sequence—and sometimes in parallel—to successfully transition ideas from initial concept to established value. Different stages of the value creation process demand different archetype strengths, and premature application of later-stage archetypes can stifle innovation, while delayed application of later-stage archetypes can prevent scaling and optimization.

Misalignments between leadership archetypes and organizational needs significantly impact strategic outcomes. When Hunters lead initiatives requiring stability, or Farmers direct breakthrough innovation efforts, organizations struggle to achieve their objectives despite talented leadership.

The Cyclical Prominence of Leadership Archetypes

There is a natural cycle to the prominence of different archetypes that aligns with business trends and organizational life cycles. Understanding this rhythm helps leaders proactively cultivate the right capabilities at the right time rather than reactively responding to challenges.

The Organizational Life Cycle and Archetype Demand

Startup/Launch Phase

Dominant Archetypes: Hunters → Scouts

During initial market entry, organizations typically need Hunter energy to drive innovation and bold market positioning. As the business establishes initial traction, Scout capabilities become increasingly important to systematically map the opportunity landscape and identify sustainable paths forward.

Growth Phase

Dominant Archetypes: Scouts → Gardeners

As organizations scale, the methodical exploration of Scouts helps identify optimal growth vectors. As these vectors are confirmed, Gardeners become essential to build adaptable systems that can flex with rapid growth while maintaining quality and culture.

Maturity Phase

Dominant Archetypes: Gardeners → Farmers

When organizations reach market saturation, Gardeners help optimize existing operations while maintaining adaptability. As markets stabilize, Farmer capabilities become increasingly valuable to systematize operations, maximize efficiency, and establish predictable performance.

Renewal/Disruption Phase

Dominant Archetypes: Farmers → Hunters

As markets eventually face disruption or saturation, organizations that have become dominated by Farmer mindsets often struggle. This creates renewed demand for Hunter archetypes who can identify breakthrough opportunities and lead bold repositioning efforts.

Figure 6: Organizations need different leadership archetype capacities as they mature.

Industry Trend Cycles and Archetype Demand

Beyond organizational life cycles, broader industry and economic trends also influence which archetypes provide the most strategic value:

During Technological Disruption

Critical Archetypes: Hunters and Scouts

When new technologies emerge (e.g., AI, blockchain, quantum computing), Hunters who can rapidly experiment with applications provide early advantages. Scouts who can systematically map implications and develop comprehensive strategies help organizations navigate complexity.

During Economic Contraction

Critical Archetypes: Farmers and Gardeners

During downturns, Farmers who can establish cost discipline and operational efficiency become essential. Gardeners who can adapt existing systems to changing resource constraints while maintaining core capabilities help organizations remain resilient.

During Regulatory Change

Critical Archetypes: Scouts and Farmers

When regulatory landscapes shift, Scouts who can methodically analyze implications and compliance requirements become vital. Farmers who can implement systematic responses and establish new operational norms help organizations mitigate risk.

During Market Consolidation

Critical Archetypes: Gardeners and Hunters

As industries consolidate, Gardeners who can adaptively integrate systems and cultures provide integration advantages. Hunters who can identify distinctive competitive positions in increasingly crowded markets help organizations maintain differentiation.

Optimizing Leadership Effectiveness for Different Archetypes Through Structure and Incentives

Different archetypes thrive in different organizational environments and respond to different incentive systems. Misalignment between an archetype’s natural tendencies and their environment often leads to underperformance despite strong talent.

Figure 7: Different leadership archetypes require different kinds of support.

Building a Balanced Leadership Portfolio

The most successful organizations deliberately construct leadership teams with complementary archetypes aligned to their strategic contexts. Key practices include:

1. Maintaining archetype diversity in leadership teams regardless of current priorities

2. Developing archetype versatility through targeted development experiences

3. Creating cross-archetype collaboration mechanisms that leverage complementary strengths

4. Implementing early warning systems that signal when archetype needs are shifting

5. Establishing succession planning that accounts for anticipated archetype requirements

Conclusion: The New Leadership Imperative is “Both-And > Either-Or”

In an era where businesses must simultaneously explore new frontiers while optimizing existing operations, understanding and deploying the right leadership archetypes becomes a critical competitive advantage. Organizations that master this framework create agile leadership portfolios capable of navigating complexity with precision.

As illustrated in Figure 5, the journey from concept discovery to mature delivery requires all four archetypes working in concert, each contributing their unique strengths at appropriate phases of development. The Hunter’s vision, the Scout’s validation, the Gardener’s scalable frameworks, and the Farmer’s optimization all play essential roles in successful value creation.

Organizations that understand these cyclical patterns gain substantial advantages in talent strategy. Rather than reactively shifting leadership approaches after conditions change, they can proactively develop and deploy the right archetype capabilities in anticipation of emerging needs.

There is no best archetype (unless it’s your archetype 😉). But you need strength in the right archetypes, in the right roles, at the right times. Don’t send Bubbles when you need Omar, and vice versa.

Gratitude

Sara Smucker Barnwell for coaching me on: how to see through other people’s lenses; the necessity of strong complements in teams; and the importance of model simplification.

Patti Worley, Candice Carr, and Lydia Petrakis for taking me on adventures that revealed these archetypal patterns.

Casey Flaherty for incepting me into this inquiry with our “The Wire” conversations.

Jae Um for provoking me to examine Rumsfeld.

Chris Clearfield for helpful feedback on Rumsfeld alignments.

Alex Hamilton for directing me to instrumental Simon Wardley content (”Wardley...innit?”).

References

Collective Genius by Linda A. Hill, Greg Brandeau, Emily Truelove and Kent Lineback

The Exploration-Exploitation Dilemma: A Multidisciplinary Framework by Oded Berger-Tal, Jonathan Nathan, Ehud Meron, David Saltz

On Pioneers, Settlers, Town Planners and Theft. by Simon Wardley

Commandos, Infantry, and Police by Jeff Atwood

The Decision Book: Fifty Models for Strategic Thinking by Mikael Krogerus and Roman Tschäppeler

The Rumsefeld Matrix

The Johari Window

A Leader’s Framework for Decision Making by David J. Snowden and Mary E. Boone

The Two Big Questions

We can place leaders on two big question lines:

1. Are they looking for new value or improving what already exists? (exploration vs. optimization)

2. Do they follow strict plans or adapt as they go? (systematic vs. adaptive)

When we cross these questions, we get four styles.

Scouts: Careful Explorers

• live in the quadrant for exploration + systematic

• gather facts, map the situation, and plan the next steps

• great for research, strategy, and reducing risk

• may move slowly when fast action is needed

Hunters: Bold Opportunity Finders

• live in the quadrant for exploration + adaptive

• chase new ideas and jump on openings quickly

• useful when the future is unclear and change is constant

• can create confusion if the work needs stability and routine

Farmers: Reliable Optimizers

• live in the quadrant for optimization + systematic

• build strong systems and make results repeatable

• shine when the job calls for consistency and efficiency

• may resist change when the world shifts

Gardeners: Flexible Improvers

• live in the quadrant for optimization + adaptive

• keep daily work humming while making small, steady upgrades

• great for leading teams through change without losing balance

• may struggle when problems require strict rules or deep study

How These Styles Fit the Value Journey

Every new idea moves through common phases. Different styles lead best at each step:

1. Discovery (Hunters) – Spot the idea.

2. Validation (Scouts) – Check that the idea can work.

3. Scaling (Gardeners) – Grow the idea while staying flexible.

4. Optimization (Farmers) – Lock in reliable processes and quality.

If you use these styles too early or too late, progress can stall. For example, let Farmers lead discovery too soon and they may shut down bold ideas. Bring in Hunters too late and they may upset a stable system.

Matching Styles to the Organization’s Life Cycle

Organizations move through stages, much like people do. As they grow, the mix of leadership styles they need also changes.

1. Startup or Launch

• Main styles: Hunters first, then Scouts

• Hunters drive quick wins and early experiments. Once there is traction, Scouts add structure and research.

2. Growth

• Main styles: Scouts first, then Gardeners

• Scouts help pick the best paths forward. Gardeners build systems that can flex and keep up with demand.

3. Maturity

• Main styles: Gardeners first, then Farmers

• Gardeners maintain quality while tuning processes. Farmers then push for efficiency and reliability.

4. Renewal or Disruption

• Main styles: Farmers first, then Hunters

• Farmers notice when existing methods stop working. Hunters return to find the next big opportunity.

Why This Matters

When leaders match the right style to the moment, organizations create value faster and smarter. When the styles are misaligned, even strong teams can struggle. Understanding these four leadership types helps leaders choose the best mix of people for each challenge.